This blog is totally independent, unpaid and has only three major objectives.
The first is to inform readers of news and happenings in the e-Health domain, both here in Australia and world-wide.
The second is to provide commentary on e-Health in Australia and to foster improvement where I can.
The third is to encourage discussion of the matters raised in the blog so hopefully readers can get a balanced view of what is really happening and what successes are being achieved.

Thursday, September 29, 2011

This Is A Really Important Reason To Make Sure EHRs Are Fully Implemented And Then Properly Exploited!

I was reminded by a series of articles this week that there was more to the push for deployment of clinician used EHRs that just assisting individual care directly.

The health battles of millions, recorded digitally, open a world of virtual research.

By Neil Savage

The antidepressant Paxil was approved for sale in 1992, the cholesterol-lowering drug Pravachol in 1996. Company studies proved that each drug, on its own, works and is safe. But what about when they are taken together?

By mining tens of thousands of electronic patient records, researchers at Stanford University quickly discovered an unexpected answer: people who take both drugs have higher blood glucose levels. The effect was even greater in diabetics, for whom excess blood sugar is a health danger.

The research is an example of the increasing ease with which scientists now scour digitized medical results, like glucose tests and drug prescriptions, to find hidden patterns. "You're not constrained by the need to actually get patients lined up in a clinical trial that would be incredibly expensive," says Russ Altman, director of Stanford's Biomedical Informatics Training Program, whose group published the Paxil/Pravachol result in the journal Clinical Pharmacology and Therapeutics this July. "We had most of this paper done probably in a month."

The spread of electronic patient records, with their computer-readable entries, is opening new possibilities for medical data mining. Instead of being limited to carefully planned studies on volunteers, scientists can increasingly carry out research virtually by sifting through troves of data collected from the unplanned experiments of real life, as preserved in medical records from scores of hospitals.

Such techniques are allowing researchers to ask questions never envisioned at the time of a drug's approval, such as how a medicine might affect particular ethnicities. They are also being used to uncover evidence of economic problems, such as overbilling and unnecessary procedures. Mining of health records "is going to build advancements in research, but also efficiencies in the health delivery system," says Margaret Anderson, executive director of FasterCures, a think tank in Washington, D.C.

Some large hospital systems that use electronic records now employ full-time database research teams. Laurence Meyer, associate chief of staff for research at the Salt Lake City Veterans Administration Medical Center, says he knows of more than 100 research projects using electronic records from the VA's six million patients, who are seen at 152 hospitals and 804 outpatient clinics across the country.

"If you're looking at a single hospital's cases of, say, hypertrophic cardiomyopathy, you might have 20 or 30 over 10 years, whereas all of a sudden we're looking at thousands of cases," says Meyer.

Large numbers of patient records are critical to these efforts, researchers say. In 2002, in the best-known case of a medical discovery to emerge from a database, researchers with the California managed-care provider Kaiser Permanente helped show that the $2.5 billion pain drug Vioxx was killing people by causing heart attacks. The effect became apparent only after Kaiser combed the records of its eight million patients. Vioxx was subsequently pulled from the market.

When recruiting patients for clinical trials, medical researchers still typically get word out with billboards and newspaper ads. Some have tried Facebook lately. But those methods generally don't attract enough of the right patients, delaying research and sometimes leading to trial cancellations.

Penn Medicine, part of the University of Pennsylvania Health System, is trying something new: using the trove of data in electronic medical records to find clinical trial candidates and then alert those patients' doctors.

It's an example of how healthcare IT teams are working to wring value from the electronic record systems they've often spent years implementing. Earlier this year Penn Medicine completed a five-year project to roll out Epic e-records to its 1,800 employed physicians. "You often need an army to install EMR systems," says Michael Restuccia, CIO of the University of Pennsylvania Health System. "But once you finally have these systems in place, the amount of effort to get added value out of them with programs like this is comparatively little."

The provider's new clinical trial application, called Penn Research Trial Advisory, took several months to develop. It has piloted the app in its ob-gyn department, helping recruit 200 patients for infertility clinical trials. The test brought an 87% increase in the number of physician-referred patients for trials compared with the prior four-month period.

"The key is the much more targeted, available information sitting in the EMR that's now turned into a tremendous asset," says Brian Wells, Penn Medicine's associate CIO. Other departments plan to use the app for their own clinical trials.

In general, 85% to 95% of patient care is provided by ambulatory care clinicians--doctors in their offices or other locations outside in-patient hospital settings. Because they tend to have a more ongoing, trusting relationship with their patients, those docs are best positioned to recruit patients for clinical trials, Restuccia says. It's more likely than an ad to produce the right conditions, as well.

As healthcare moves gradually from a fee-for-service to a pay-for performance model, it will be judged on how closely clinicians adhere to evidence-based guidelines. The best evidence comes from controlled clinical trials, but since so few treatment protocols are supported by these trials, the movers and shakers in clinical medicine are looking to fill the void by recruiting large groups of subjects willing to enroll in them. Not an easy task.

What has surprised many IT managers and clinicians is how valuable EHRs are proving to be in fueling that research effort.

The Mayo Clinic, for instance, has been mining its EHR data for several years to find subjects for clinical trials. When choosing patients to enroll in such experiments, one of the challenges is to find those who meet predetermined clinical criteria. Sifting through paper files is a nightmare, and even having clinicians manually search e-records for eligible candidates takes far too long.

With that in mind, Mayo Clinic has employed natural language processing (NLP) to speed things along. In one project, it needed to locate patients with heart failure to enroll in a study. The NLP-enabled algorithm was engineered to search through EHRs--including free-text clinician notes--to locate patients with cardiomyopathy, congestive heart failure, pulmonary edema, and a variety of other relevant conditions.

But the system didn't stop there. It automatically searched for synonyms in a database of 16 million problem list entries--all of which were written in unstructured natural language. The same algorithm was capable of weeding out patients with "negation indicators"--patients whose records said something like "patient denies symptoms of heart failure."